NeuronGroup class¶
(Shortest import: from brian2 import NeuronGroup)
-
class
brian2.groups.neurongroup.
NeuronGroup
(N, model, method=('exact', 'euler', 'heun'), method_options=None, threshold=None, reset=None, refractory=False, events=None, namespace=None, dtype=None, dt=None, clock=None, order=0, name='neurongroup*', codeobj_class=None)[source]¶ Bases:
brian2.groups.group.Group
,brian2.core.spikesource.SpikeSource
A group of neurons.
Parameters: N : int
Number of neurons in the group.
model : (str,
Equations
)The differential equations defining the group
method : (str, function), optional
The numerical integration method. Either a string with the name of a registered method (e.g. “euler”) or a function that receives an
Equations
object and returns the corresponding abstract code. If no method is specified, a suitable method will be chosen automatically.threshold : str, optional
The condition which produces spikes. Should be a single line boolean expression.
reset : str, optional
The (possibly multi-line) string with the code to execute on reset.
refractory : {str,
Quantity
}, optionalEither the length of the refractory period (e.g.
2*ms
), a string expression that evaluates to the length of the refractory period after each spike (e.g.'(1 + rand())*ms'
), or a string expression evaluating to a boolean value, given the condition under which the neuron stays refractory after a spike (e.g.'v > -20*mV'
)events : dict, optional
User-defined events in addition to the “spike” event defined by the
threshold
. Has to be a mapping of strings (the event name) to strings (the condition) that will be checked.namespace: dict, optional :
A dictionary mapping identifier names to objects. If not given, the namespace will be filled in at the time of the call of
Network.run()
, with either the values from thenamespace
argument of theNetwork.run()
method or from the local context, if no such argument is given.dtype : (
dtype
,dict
), optionalThe
numpy.dtype
that will be used to store the values, or a dictionary specifying the type for variable names. If a value is not provided for a variable (or no value is provided at all), the preference setting core.default_float_dtype is used.codeobj_class : class, optional
The
CodeObject
class to run code with.dt :
Quantity
, optionalThe time step to be used for the simulation. Cannot be combined with the
clock
argument.clock :
Clock
, optionalThe update clock to be used. If neither a clock, nor the
dt
argument is specified, thedefaultclock
will be used.order : int, optional
The priority of of this group for operations occurring at the same time step and in the same scheduling slot. Defaults to 0.
name : str, optional
A unique name for the group, otherwise use
neurongroup_0
, etc.Notes
NeuronGroup
contains aStateUpdater
,Thresholder
andResetter
, and these are run at the ‘groups’, ‘thresholds’ and ‘resets’ slots (i.e. the values of theirwhen
attribute take these values). Theorder
attribute will be passed down to the contained objects but can be set individually by setting theorder
attribute of thestate_updater
,thresholder
andresetter
attributes, respectively.Attributes
_refractory
The refractory condition or timespan event_codes
Code that is triggered on events (e.g. events
Events supported by this group method_choice
The state update method selected by the user namespace
The group-specific namespace resetter
Reset neurons which have spiked (or perform arbitrary actions for user-defined events) spikes
The spikes returned by the most recent thresholding operation. state_updater
Performs numerical integration step subexpression_updater
Update the “constant over a time step” subexpressions thresholder
Checks the spike threshold (or abitrary user-defined events) user_equations
The original equations as specified by the user (i.e. Methods
before_run
([run_namespace])Optional method to prepare the object before a run. run_on_event
(event, code[, when, order])Run code triggered by a custom-defined event (see NeuronGroup
documentation for the specification of events).The createdResetter
object will be automatically added to the group, it therefore does not need to be added to the network manually.set_event_schedule
(event[, when, order])Change the scheduling slot for checking the condition of an event. state
(name[, use_units, level])Return the state variable in a way that properly supports indexing in the context of this group Details
-
_refractory
¶ The refractory condition or timespan
-
event_codes
¶ Code that is triggered on events (e.g. reset)
-
events
¶ Events supported by this group
-
method_choice
¶ The state update method selected by the user
-
namespace
¶ The group-specific namespace
-
resetter
¶ Reset neurons which have spiked (or perform arbitrary actions for user-defined events)
-
spikes
¶ The spikes returned by the most recent thresholding operation.
-
state_updater
¶ Performs numerical integration step
-
subexpression_updater
¶ Update the “constant over a time step” subexpressions
-
thresholder
¶ Checks the spike threshold (or abitrary user-defined events)
-
user_equations
¶ The original equations as specified by the user (i.e. without the multiplied
int(not_refractory)
term for equations marked as(unless refractory)
)
-
run_on_event
(event, code, when='after_resets', order=None)[source]¶ Run code triggered by a custom-defined event (see
NeuronGroup
documentation for the specification of events).The createdResetter
object will be automatically added to the group, it therefore does not need to be added to the network manually. However, a reference to the object will be returned, which can be used to later remove it from the group or to set it to inactive.Parameters: event : str
The name of the event that should trigger the code
code : str
The code that should be executed
when : str, optional
The scheduling slot that should be used to execute the code. Defaults to
'after_resets'
.order : int, optional
The order for operations in the same scheduling slot. Defaults to the order of the
NeuronGroup
.Returns: obj :
Resetter
A reference to the object that will be run.
-
set_event_schedule
(event, when='after_thresholds', order=None)[source]¶ Change the scheduling slot for checking the condition of an event.
Parameters: event : str
The name of the event for which the scheduling should be changed
when : str, optional
The scheduling slot that should be used to check the condition. Defaults to
'after_thresholds'
.order : int, optional
The order for operations in the same scheduling slot. Defaults to the order of the
NeuronGroup
.
-
state
(name, use_units=True, level=0)[source]¶ Return the state variable in a way that properly supports indexing in the context of this group
Parameters: name : str
The name of the state variable
use_units : bool, optional
Whether to use the state variable’s unit.
level : int, optional
How much farther to go down in the stack to find the namespace.
Returns :
——- :
var :
VariableView
or scalar valueThe state variable’s value that can be indexed (for non-scalar values).
-
Tutorials and examples using this¶
- Tutorial 2-intro-to-brian-synapses
- Tutorial 1-intro-to-brian-neurons
- Tutorial 3-intro-to-brian-simulations
- Example IF_curve_LIF
- Example reliability
- Example non_reliability
- Example phase_locking
- Example IF_curve_Hodgkin_Huxley
- Example adaptive_threshold
- Example COBAHH
- Example CUBA
- Example advanced/stochastic_odes
- Example advanced/opencv_movie
- Example advanced/float_32_64_benchmark
- Example advanced/custom_events
- Example advanced/compare_GSL_to_conventional
- Example frompapers/Brette_Guigon_2003
- Example frompapers/Clopath_et_al_2010_homeostasis
- Example frompapers/Brunel_Hakim_1999
- Example frompapers/Platkiewicz_Brette_2011
- Example frompapers/Clopath_et_al_2010_no_homeostasis
- Example frompapers/Diesmann_et_al_1999
- Example frompapers/Touboul_Brette_2008
- Example frompapers/Izhikevich_2007
- Example frompapers/Brette_2004
- Example frompapers/Sturzl_et_al_2000
- Example frompapers/Brette_Gerstner_2005
- Example frompapers/Brunel_Wang_2001
- Example frompapers/Morris_Lecar_1981
- Example frompapers/Rossant_et_al_2011bis
- Example frompapers/Hindmarsh_Rose_1984
- Example frompapers/Kremer_et_al_2011_barrel_cortex
- Example frompapers/Wang_Buszaki_1996
- Example frompapers/Vogels_et_al_2011
- Example frompapers/Rothman_Manis_2003
- Example frompapers/Stimberg_et_al_2018/example_4_synrel
- Example frompapers/Stimberg_et_al_2018/example_6_COBA_with_astro
- Example frompapers/Stimberg_et_al_2018/example_3_io_synapse
- Example frompapers/Stimberg_et_al_2018/example_4_rsmean
- Example frompapers/Stimberg_et_al_2018/example_2_gchi_astrocyte
- Example frompapers/Stimberg_et_al_2018/example_5_astro_ring
- Example frompapers/Stimberg_et_al_2018/example_1_COBA
- Example synapses/nonlinear
- Example synapses/gapjunctions
- Example synapses/licklider
- Example synapses/spatial_connections
- Example synapses/state_variables
- Example synapses/synapses
- Example synapses/efficient_gaussian_connectivity
- Example synapses/STDP
- Example synapses/jeffress
- Example standalone/STDP_standalone
- Example standalone/cuba_openmp
- Example compartmental/bipolar_with_inputs
- Example compartmental/bipolar_with_inputs2
- Example compartmental/lfp